Bell sampling in Quantum Monte Carlo simulations

Quantum Monte Carlo (QMC) methods are essential for the numerical study of large-scale quantum many-body systems, yet their utility has been significantly hampered by the difficulty in computing key quantities such as off-diagonal operators and entanglement. This work introduces Bell-QMC, a novel QMC framework leveraging Bell sampling, a two-copy measurement protocol in the transversal Bell basis. We demonstrate that Bell-QMC enables an efficient and unbiased estimation of both challenging classes of observables, offering a significant advantage over previous QMC approaches. Notably, the entanglement across all system partitions can be computed in a single Bell-QMC simulation. We implement this method within the stochastic series expansion (SSE), where we design an efficient update scheme for sampling the configurations in the Bell basis. We demonstrate our algorithm in the one-dimensional transverse-field Ising model and the two-dimensional $\mathbb{Z}_2$ lattice gauge theory, extracting universal quantum features using only simple diagonal measurements. This work establishes Bell-QMC as a powerful framework that significantly expands the accessible quantum properties in QMC simulations, providing an exponential advantage over conventional QMC.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-20
DOI
https://doi.org/10.5281/zenodo.15470953
Citations
1
Primary Topic
Quantum many-body systems
Type
article
Field-Weighted Citation Impact
1.25
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Bell sampling in Quantum Monte Carlo simulations

1 citations
Zenodo (CERN European Organization for Nuclear Research)
Quantum many-body systems
1.25
article

Bell sampling in Quantum Monte Carlo simulations

article en
1 citations

Abstract

Quantum Monte Carlo (QMC) methods are essential for the numerical study of large-scale quantum many-body systems, yet their utility has been significantly hampered by the difficulty in computing key quantities such as off-diagonal operators and entanglement. This work introduces Bell-QMC, a novel QMC framework leveraging Bell sampling, a two-copy measurement protocol in the transversal Bell basis. We demonstrate that Bell-QMC enables an efficient and unbiased estimation of both challenging classes of observables, offering a significant advantage over previous QMC approaches. Notably, the entanglement across all system partitions can be computed in a single Bell-QMC simulation. We implement this method within the stochastic series expansion (SSE), where we design an efficient update scheme for sampling the configurations in the Bell basis. We demonstrate our algorithm in the one-dimensional transverse-field Ising model and the two-dimensional $\mathbb{Z}_2$ lattice gauge theory, extracting universal quantum features using only simple diagonal measurements. This work establishes Bell-QMC as a powerful framework that significantly expands the accessible quantum properties in QMC simulations, providing an exponential advantage over conventional QMC.

Zenodo (CERN European Organization for Nuclear Research)
Fudan University (CN), Westlake University (CN), Munich Center for Quantum Science and Technology (DE), Technical University of Munich (DE)
Industry, innovation and infrastructure
Openalex Percentile: Top 15%
Quantum many-body systems
1.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Bell sampling in Quantum Monte Carlo simulations · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS